现代信息科技2025，Vol.9Issue(8)：10-15,6.DOI:10.19850/j.cnki.2096-4706.2025.08.003
基于TimeGAN的轨道交通LTE-M故障预测研究
Research on LTE-M Fault Prediction of Rail Transit Based on TimeGAN
摘要
Abstract
The Long Term Evolution of Metro(LTE-M)network fault prediction dataset of rail transit has the problems of unbalanced samples and small amount of sample data,which impact the accuracy of fault prediction.In order to solve the above problems,this paper proposes a research method of LTE-M fault prediction of rail transit based on conditional Time-series Generative Adversarial Networks(TimeGAN).By defining dynamic autoencoder and static autoencoder in TimeGAN model,this method further explores the dynamic and static characteristics of LTE-M fault data of rail transit,and introduces GELU activation function in the potential space of generator and discriminator to accelerate model convergence and generate synthetic data closer to real data,thus effectively alleviating the problem of unbalanced fault dataset and small data volume.The experimental results show that when the data synthesized by the TimeGAN model is used for fault prediction training,it can produce better prediction results than the original data.关键词
轨道交通LTE-M/故障预测/时间序列/TimeGANKey words
rail transit LTE-M/fault prediction/time-series/TimeGAN分类
信息技术与安全科学引用本文复制引用
余凤琴,邹劲柏,沙宏..基于TimeGAN的轨道交通LTE-M故障预测研究[J].现代信息科技,2025,9(8):10-15,6.基金项目
轨道交通智能运维关键技术研究项目(20090503100) （20090503100）
"一带一路"中老铁路工程国际联合实验室(21210750300) （21210750300）
